Estimates of global mortality burden associated with short-term exposure to fine particulate matter (PM2·5)
Bibliographic record
Abstract
Background The acute health effects of short-term (hours to days) exposure to fine particulate matter (PM 2·5 ) have been well documented; however, the global mortality burden attributable to this exposure has not been estimated. We aimed to estimate the global, regional, and urban mortality burden associated with short-term exposure to PM 2·5 and the spatiotemporal variations in this burden from 2000 to 2019. Methods We combined estimated global daily PM 2·5 concentrations, annual population counts, country-level mortality rates, and epidemiologically derived exposure–response functions to estimate the mortality attributable to short-term PM 2·5 exposure from 2000 to 2019, in the continental regions and in 13 189 urban centres worldwide at a spatial resolution of 0·1° × 0·1°. We tested the robustness of our mortality estimates with different theoretical minimum risk exposure levels, lag effects, and exposure–response functions. Findings Approximately 1 million (95% CI 690 000–1·3 million) premature deaths per year from 2000 to 2019 were attributable to short-term PM 2·5 exposure, representing 2·08% (1·41–2·75) of total global deaths or 17 (11–22) premature deaths per 100 000 population. Annually, 0·23 million (0·15 million–0·30 million) deaths attributable to short-term PM 2·5 exposure were in urban areas, constituting 22·74% of the total global deaths attributable to this cause and accounting for 2·30% (1·56–3·05) of total global deaths in urban areas. The sensitivity analyses showed that our worldwide estimates of mortality attributed to short-term PM 2·5 exposure were robust. Interpretation Short-term exposure to PM 2·5 contributes a substantial global mortality burden, particularly in Asia and Africa, as well as in global urban areas. Our results highlight the importance of mitigation strategies to reduce short-term exposure to air pollution and its adverse effects on human health. Funding Australian Research Council and the Australian National Health and Medical Research Council.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".